Abstract
We present AttentiveLearner, an intelligent mobile learning system optimized for consuming lecture videos in both Massive Open Online Courses (MOOCs) and flipped classrooms. AttentiveLearner uses on-lens finger gestures as an intuitive control channel for video playback. More importantly, AttentiveLearner implicitly extracts learners’ heart rates and infers their attention by analyzing learners’ fingertip transparency changes during learning on today’s unmodified smart phones. In a 24-participant study, we found heart rates extracted from noisy image frames via mobile cameras can be used to predict both learners’ “mind wandering” events in MOOC sessions and their performance in follow-up quizzes. The prediction performance of AttentiveLearner (accuracy = 71.22%, kappa = 0.22) is comparable with existing research using dedicated sensors. AttentiveLearner has the potential to improve mobile learning by reducing the sensing equipment required by many state-of-the-art intelligent tutoring algorithms.
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Pham, P., Wang, J. (2015). AttentiveLearner: Improving Mobile MOOC Learning via Implicit Heart Rate Tracking. In: Conati, C., Heffernan, N., Mitrovic, A., Verdejo, M. (eds) Artificial Intelligence in Education. AIED 2015. Lecture Notes in Computer Science(), vol 9112. Springer, Cham. https://doi.org/10.1007/978-3-319-19773-9_37
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DOI: https://doi.org/10.1007/978-3-319-19773-9_37
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